Grammar Error Correction Based on Deep Learning: Incorporating Convolutional Neural Networks

Grammar error correction plays a critical role in natural language processing and automated language learning. Traditional rule-based and statistical approaches struggle with handling complex syntactic structures and semantic nuances. Recent advancements in deep learning have demonstrated promising results in grammar error correction tasks. Recurrent neural networks and Transformer based models often suffer from high computational costs and long-range dependency issues. This study proposes a novel grammar error correction framework based on convolutional neural networks (CNNs). The model leverages feature extraction capabilities to enhance error detection and correction efficiency. The proposed model processes grammatical patterns at multiple hierarchical levels. It efficiently captures local syntactic dependencies while maintaining computational efficiency. Extensive experiments on benchmark datasets demonstrate that the convolutional neural network based grammar error correction model achieves competitive performance compared to existing state-of-the-art methods. The approach improves both accuracy and processing speed. The results highlight the effectiveness of CNNs in grammar correction tasks. This research contributes to the development of more robust and practical grammar correction systems. Potential applications include educational technology, automated writing assistants and professional content editing.